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Analysis of Gene Expression at the Single-Cell Level

Analysis of Gene Expression at the Single-Cell Level. Guo-Cheng Yuan Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute Harvard School of Public Health. Bioconductor , July 31 st , 2014. bioconductor.

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Analysis of Gene Expression at the Single-Cell Level

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  1. Analysis of Gene Expression at the Single-Cell Level Guo-Cheng Yuan Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute Harvard School of Public Health Bioconductor, July 31st, 2014

  2. bioconductor

  3. Methods to sequence the DNA and RNA of single cells are poised to transform many areas of biology and medicine. --- Nature Methods

  4. “Recent technical advances have enabled RNA sequencing (RNA-seq) in single cells. Exploratory studies have already led to insights into the dynamics of differentiation, cellular responses to stimulation and the stochastic nature of transcription. We are entering an era of single-cell transcriptomics that holds promise to substantially impact biology and medicine.” • R. Sandberg, 2014. Nature Methods

  5. Cell-type A Cell-type C Cell Division Cell-type B Cell-type E Cell-type D Cell-type F

  6. R. Sandberg, 2014. Nature Methods

  7. Challenges in single-cell data analysis • Characterize and distinguish technical/biological variability • Identify new and meaningful cell clusters. • Identify the lineage relationship between different cell clusters. • Characterize the dynamic process during cell-state transitions. • Elucidate the transition of regulatory networks. • Distinguish stochastic vs real variation

  8. CMP GMP MEP CLP MEP GuojiGuo, Eugenio Marco

  9. SPADE: a density-normalized, spanning tree model Down-sample Clustering, Spanning-tree Visualization Qiu et al. 2011 Nat Biotech, p886

  10. Cancer Stem Cells • Each cancer contains a highly heterogeneous cell population. • Clonal evolution contributes to cancer heterogeneity • Cancer cells are hierarchically organized and maintained by cancer stem cells • How are the leukemia stem cells related to normal blood cell lineage? How do they differ?

  11. Single cell analysis of the mouse MLL-AF9 acute myeloid leukemia cells Compilation of mouse cell surface antigens (Lai et al., 1998; eBioscience website) Primer design for 300 multiplexed PCR (collaboration with Helen Skaletsky) Micro-fluidic high-throughput realtime PCR (96.96 Array) GuojiGuo, AssiehSaadatpour

  12. t-SNE analysis identifies similarities between cell-types • t-SNE is a nonlinear dimension reduction method, and can identify patterns undetectable by PCA • t-SNE minimizes the divergence between distributions over pairs of points. • Leukemia cells are more similar to GMPs than to HSCs • Leukemia cells are highly heterogeneous.

  13. Mapping leukemia cells to normal hematopoietic cell hierarchy • Use 33 common genes to map cell hierarchy. • Mapping identifies two subtypes of leukemia cells. • These cells are similar but not identical to their corresponding normal lineages.

  14. Coexpression networks are different among subtypes All Leukemia GMP Leukemia 2 Leukemia 1

  15. Surani and Tischler, Nature 2012 Guo et al. Dev Cell 2010

  16. Dynamic clustering T = 1 T = 2 T = 3 T = 4 Maximizing the penalized log-likelihood. Eugenio Marco, Bobby Karp, Lorenzo Trippa, GuojiGuo

  17. Identifying bifurcation points and directions EPI ICM PE TE >80% variance increase during bifurcation is attributed to a single (bifurcation) direction.

  18. Modeling dynamics by bifurcation analysis I) U(x) II) U(x)

  19. Modeling dynamics by bifurcation analysis I) U(x) II) U(x)

  20. Noise level s has large impact on lineage biases • = 1 • = 0.5 • = 2

  21. Lineage bias due to perturbation of TF activity Perturbation Control U(x) U(x) Predicted lineage bias due to 2 fold decrease of TF level

  22. Experimental validation using Nanog mutant PE EPI Nanog

  23. How do we infer dynamics without temporal information?

  24. Characterization of early bipotential progeny ofLgr5+ intestinal stem cells Crosnier 2006. Nature Review Tae-Hee Kim, AssiehSaadatpour

  25. Principal Curve Analysis Reconstruct Temporal Information t-SNE plot indicates two distinct clusters, linked a small number of transitional cells

  26. Principal Curve Analysis Reconstruct Temporal Information t-SNE plot indicates two distinct clusters, linked a small number of transitional cells Principal curve analysis captures the overall trend of cell-state transition

  27. Inferred dynamic gene expression profile Use the principal curve coordinate as a proxy for temporal evolution.

  28. Conclusions • Single-cell genomics is a powerful technology for understanding cellular heterogeneity and hierarchy. • Single-cell gene expression data analysis present many new methodological challenges. • It is a great time to develop algorithms and software for single cell data analysis.

  29. Acknowledgement Eugenio Marco AssiehSaadatpour Bobby Karp Lorenzo Trippa Paul Robson Stuart Orkin GuojiGuo RameshShivdasani Tae-Hee Kim Funding from NIH, HSCI

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